As data rate requirements of users continuously increase, and the base station miniaturization trend becomes increasingly obvious, a quantity of network elements that need to be maintained by an operator is growing rapidly, and maintenance costs that need to be invested are also increasing. In addition, due to high mobility of user applications, a mobile communications network is increasingly dynamic. A Self-Organization Network (SON) technology is proposed, expecting to achieve maximal automation in planning, deployment, and operation and maintenance phases of the mobile communications network, so as to achieve objectives of reducing operating costs and improving key performance indicator (KPI) of the network.
Application of the SON technology is based on identifying of a network status, that is, a network node first needs to learn a current running status of a network, so as to provide necessary information for the SON technology, for example, which SON operations are triggered. Therefore, how to obtain, by analyzing, the current running status of the network is a prerequisite for SON application.
In an existing SON-based cellular network, an operator learns a current running status of the network through KPI statistics of a network management system. When a KPI statistics value of the network exceeds a range preset by the operator, that is, when network performance cannot reach a preset value, operation and maintenance and network management personnel learn, by using an empirical analysis method, a problem that may occur during network running, that is, which network parameters are improperly configured, so as to trigger a corresponding optimization algorithm. After parameter configuration output by the optimization algorithm is applied on a network device, network KPI statistics are continuously collected and reported to the network management system. Operations of KPI statistics reporting, performance alarming, fault analysis, parameter configuration, and KPI running and statistics collection continuously cycle, so as to implement maintenance of the cellular network.
However, representing a current running status of a network only by using a KPI cannot reflect a problem with the network, and manual analysis is required. Therefore, a huge investment of human resources costs and expert knowledge are required. In addition, an optimization algorithm that is triggered only by KPIs cannot ensure that a corresponding effect is achieved.